Occlusion caused by gathering of marine creatures together is an important reason for false and missed detections. Therefore, this study proposes a marine creature detection method based on iterative fusion of sample-assisted network training. First, an improved deep hole residual structure is selected as the feature extraction network, which improves the feature extraction ability of the network. Second, because of the occlusion and dense characteristics of marine creature images, the loss function is improved to avoid false and missed detections. Finally, to solve the problems of target occlusion and data imbalance, a sample iterative fusion method is proposed to generate an extended training set of simulated images. This improves the effectiveness of network training and the ability to detect marine creatures with a small sample size. The experimental results show that the proposed method can achieve a detection accuracy of 91.36% on the URPC2018 dataset and 90.27% on the Taiwan fish dataset. The detection accuracy and speed of the proposed method are higher than those of existing target detection algorithms.